SVM

Before moving forward with the to-do list, let’s throw a Random Forest to it.

SVM

For many reasons, Random Forest is usually a very good baseline model. In this particular case I started with the polynomial OLS as baseline model, just because it was so evident from the correlations that the relationship between temperature and consumption follows a polynomial shape. But let’s go back to a beloved RF.

/home/runner/work/strom/strom/.venv/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

Model Cards provide a framework for transparent, responsible reporting. 
 Use the vetiver `.qmd` Quarto template as a place to start, 
 with vetiver.model_card()
Writing pin:
Name: 'wd-svm'
Version: 20251122T162915Z-1869e

♻️  stepit 'svm_raw': is up-to-date. Using cached result for `strom.modelling.assess_model()` 2025-11-22 16:29:15

Metrics

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	2.432282	2.419931	2.673258	3.329209
MSE - Mean Squared Error	18.289605	22.503780	24.400576	23.520673
RMSE - Root Mean Squared Error	4.276635	4.743815	3.990919	4.776235
R2 - Coefficient of Determination	0.805274	0.749650	0.301124	0.760764
MAPE - Mean Absolute Percentage Error	0.193486	0.200061	0.203410	0.343579
EVS - Explained Variance Score	0.824572	0.789548	0.611400	0.820277
MeAE - Median Absolute Error	1.566992	1.308510	1.987998	2.657292
D2 - D2 Absolute Error Score	0.648420	0.643352	0.214133	0.531369
Pinball - Mean Pinball Loss	1.216141	1.209965	1.336629	1.664605

Scatter plot matrix

Observed vs. Predicted and Residuals vs. Predicted

Check for …

check the residuals to assess the goodness of fit.

white noise or is there a pattern?
heteroscedasticity?
non-linearity?

Normality of Residuals:

Check for …

Are residuals normally distributed?

Leverage

Scale-Location plot

Residuals Autocorrelation Plot

Residuals vs Time

Well, not that bad, but it is overfitting quite a lot.

♻️  stepit 'grid_search_pipe': is up-to-date. Using cached result for `strom.modelling.grid_search_pipe()` 2025-11-22 16:29:19

Model Cards provide a framework for transparent, responsible reporting. 

 Use the vetiver `.qmd` Quarto template as a place to start, 

 with vetiver.model_card()

Writing pin:

Name: 'wd-svm'

Version: 20251122T162919Z-50658

♻️  stepit 'svm_tuned': is up-to-date. Using cached result for `strom.modelling.assess_model()` 2025-11-22 16:29:19

Metrics

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	2.247125	2.106274	2.047927	2.378226
MSE - Mean Squared Error	16.223860	19.443720	19.789512	16.932822
RMSE - Root Mean Squared Error	4.027885	4.409503	3.365674	4.111345
R2 - Coefficient of Determination	0.827268	0.783693	0.583568	0.826061
MAPE - Mean Absolute Percentage Error	0.184460	0.203015	0.152578	0.185927
EVS - Explained Variance Score	0.827974	0.791971	0.657677	0.827940
MeAE - Median Absolute Error	1.332851	1.088117	1.345858	1.470077
D2 - D2 Absolute Error Score	0.675184	0.689578	0.402628	0.663515
Pinball - Mean Pinball Loss	1.123562	1.053137	1.023964	1.189113